Modelling interannual variation in the spring and autumn land surface  phenology of the European forest

Rodriguez-Galiano, Victor F.; Sanchez-Castillo, Manuel; Dash, Jadunandan; Atkinson, Peter M.; Ojeda-Zujar, Jose

doi:10.5194/bg-13-3305-2016

Articles | Volume 13, issue 11

https://doi.org/10.5194/bg-13-3305-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/bg-13-3305-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 13, issue 11

Research article

|

06 Jun 2016

Research article |

| 06 Jun 2016

Modelling interannual variation in the spring and autumn land surface phenology of the European forest

Victor F. Rodriguez-Galiano, Manuel Sanchez-Castillo, Jadunandan Dash, Peter M. Atkinson, and Jose Ojeda-Zujar

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Month	HTML	PDF	XML	Total
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Feb 2025	25	13	2	40
Mar 2025	22	12	3	37
Apr 2025	9	12	1	22
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Jun 2025	35	17	0	52
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Cumulative views and downloads (calculated since 30 Jul 2015)

Month	HTML	PDF	XML	Total
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Aug 2015	32	35	1	68
Sep 2015	10	9	2	21
Oct 2015	15	9	2	26
Nov 2015	11	16	0	27
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Feb 2016	8	4	0	12
Mar 2016	13	5	1	19
Apr 2016	6	3	0	9
May 2016	6	2	0	8
Jun 2016	88	40	9	137
Jul 2016	19	13	1	33
Aug 2016	19	13	3	35
Sep 2016	13	6	1	20
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Feb 2017	9	5	0	14
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Dec 2024	20	8	5	33
Jan 2025	26	13	2	41
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Mar 2025	22	12	3	37
Apr 2025	9	12	1	22
May 2025	23	12	0	35
Jun 2025	35	17	0	52
Jul 2025	24	18	3	45
Aug 2025	18	15	1	34
Sep 2025	34	20	1	55
Oct 2025	35	29	6	70
Nov 2025	37	21	13	71

Latest update: 25 Nov 2025

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Short summary

This research reveals new insights into the weather drivers of land surface phenology (LSP) across the entire European forest, while at the same time it establishes a new conceptual framework for modelling LSP. Specifically, a sophisticated machine learning regression method (RF) was introduced for LSP modelling across very large areas and across multiple years simultaneously. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP interannual variation.